As part of our team assignment for the module on big data, I was looking at what kind of environment data we can find so that we can practice the use of using BigQuery to experience the full ELT pipeline.

Here's the prompt I used.

Hi Claude you are an environmental consultant tasked to study how air quality of a country is affected by the activities within in, and you are doing a big data analysis to understand, rank and visualize the top 5 industry polluters. You are also to compare these polluters across different countries and show how, if any, what quantifiable improvements made by each country and their results. Guide me through step by step using Google's Cloud Platform's Big Query to ingest a free dataset through API or other real-time data ingestion, preferably streaming, then use a suitable data warehouse to store the data and then do ELT pipelines, then create some prediction models and generate reports through the transformed data. Your final output should be a modern dashboard that can be presented to the United Nations meeting to share the challenges of balancing advancement vs air quality and how poor business decisions can lead to bad air quality and eventually poor health of the people.

So now, it's day 2 and we are working on creating a workflow of using Docker to run Spark to get Air Quality data through API. I'll keep you updated on what happens.

#AirQuality #DataScience #BigQuery #MachineLearning #EnvironmentalData #GoogleCloud #ClaudeAI #DataAnalysis #Sustainability #TechJourney